Rotation Invariant Image Description with Local Binary Pattern Histogram Fourier Features

  • Authors:
  • Timo Ahonen;Jiří Matas;Chu He;Matti Pietikäinen

  • Affiliations:
  • Machine Vision Group, University of Oulu, Finland;Center for Machine Percpetion, Dept. of Cybernetics, Faculty of Elec. Eng., Czech Technical University in Prague,;School of Electronic Information, Wuhan University, P.R. China and Machine Vision Group, University of Oulu, Finland;Machine Vision Group, University of Oulu, Finland

  • Venue:
  • SCIA '09 Proceedings of the 16th Scandinavian Conference on Image Analysis
  • Year:
  • 2009

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Abstract

In this paper, we propose Local Binary Pattern Histogram Fourier features (LBP-HF), a novel rotation invariant image descriptor computed from discrete Fourier transforms of local binary pattern (LBP) histograms. Unlike most other histogram based invariant texture descriptors which normalize rotation locally, the proposed invariants are constructed globally for the whole region to be described. In addition to being rotation invariant, the LBP-HF features retain the highly discriminative nature of LBP histograms. In the experiments, it is shown that these features outperform non-invariant and earlier version of rotation invariant LBP and the MR8 descriptor in texture classification, material categorization and face recognition tests.